Looking for the best algorithm in the diabetes population for advanced fibrosis detection: The best is the enemy of the good
Authors: Manuel Romero-Gómez, Carmen Lara
Journal: Hepatology (2023)

Looking for the best algorithm to select patients at risk of MASH and advanced fibrosis is a major unmet need.1 A perfect equilibrium between false negative rate and referral rate is the main goal to avoid the disappointing task of weed out patients without risk. In the current issue, Ajmera et al2 reported interesting data from a well-defined cohort of patients with type 2 diabetes comparing 3 clinical pathways: the one proposed by AGA [using fibrosis-4 (FIB4) and vibration-controlled transient elastography (VCTE)], the one proposed by AASLD [using FIB4 and enhanced liver fibrosis (ELF)], and a third one AASLD but using higher cutoff points for ELF, which has been reported to increase ELF specificity.3 The AGA pathway showed the lowest false negative rate and the best referral rate, assuming it is the most accurate pathway to detect advanced fibrosis. However, VCTE could have limited access in endocrinology clinics and primary care units. Thus, it could be useful to generate access to tools to rule out patients without advanced fibrosis before getting specialized care by using only blood-based tests. In this sense, the AASLD pathway was developed as 2 blood-based tests (FIB4 and ELF).4 Indeed, the initial cutoffs for ELF (<7.7, 7.7–9.8, >9.8) have been controversial. Despite inclusion in the AASLD guidelines, there is very limited data supporting the use of 7.7 cutoff; on the contrary, clinical evidence supports the use of 9.8 and 11.3 cutoffs,5 as confirmed in the current study showing 0% of cases with ELF score <7.7. By using higher cutoffs (9.8 and 11.3), as shown in the third arm of the study, unnecessary referrals decrease by up to 20% without significantly increasing the false negative rate. The same benefit has been described previously by Srivastava et al6, decreasing unnecessary referrals by 80% and improving the detection of cases with advanced fibrosis and cirrhosis. We can learn many interesting key points from this intriguing study: (1) False negative rate lower than 5% is an achievable threshold and support strongly its use in clinical practice. Some authors claimed FIB4 <1.3 could have many drawbacks in cohorts with metabolic derangements, mainly in the diabetic population. A tenth of patients with advanced fibrosis demonstrated by liver biopsy could score FIB4 <1.3.7 Indeed, using VCTE as a reference method, in a cohort of 5129 patients, FIB4<1.3 was found in 11% of patients living with type 2 diabetes.8 However, in the current study, comparing with magnetic resonance elastography (MRE) false negative rate was under 5%, saving these concerns. Using MRE as a reference method FIB4 in the diabetic population seems to improve performance in comparison with liver biopsy or transient elastography as comparator. Some groups proposed lowering the threshold to 1.0 to identify these patients. However, the burden of patients would increase and final cost-effectivity decline. Anyway, how to improve the performance of FIB4 as first-line test is controversial, and no consensus is available yet. (2) As stated before, ELF's thresholds should be replaced due to inconsistency and large rate of false positive results when using 7.7 and should be moved upper to use in clinical practice 9.8 as the lower cutoff and 11.3 as the high-risk cutoff to reach a better balance between false negative and referral rate. (3) Combination of ELF or VCTE according to the availability with FIB4 generates a double clinical pathway with an acceptable balance between false negative rate and referral rate and allows centers to choose between both according to availability. VCTE seems to be significantly superior to ELF, but this test has the advantage that could be used as a reflex test in patients with altered FIB4. Lastly, when available, as reported in the current issue of Hepatology, the sequential combination of these 3 tests FIB4, ELF, and TE reached the best scores of accuracy. (4) To implement these clinical pathways out of liver unit, it makes sense to use just one cutoff segregating patients at risk of suffering from advanced fibrosis and thus requiring referral to hepatology units. Using just the lower cutoff (FIB4 <1.3; VCTE <8 kPa and ELF <9.8) could be easy to manage and implement them in primary care, endocrinology, or internal medicine units. Patients showing scores over these cutoffs should be referred. However, patients with scores under these cutoffs should be managed out of liver units and do not need to be referred. Indeed, patients showing FIB4 >2.67 could benefit from a second-line test to better stratify fibrosis risk due to around half of patients showed no advanced fibrosis. (5) MRE seems to have reached enough scientific evidence to be used as gold standard for the diagnosis of advanced fibrosis. In a recent individual patient data metanalysis, the diagnosis accuracy was confirmed over 0.92 in AUROC, and new thresholds emerged to confirm advanced fibrosis at 3.53 kPa, slightly lower than the cutoff included in the current study.9 The association between raised stiffness by MRE and liver-related outcomes has also been reported to be similar or even superior to histological fibrosis.10 MRE analyses the whole liver and could be repeated over time to better stratify fibrosis risk. The gold standard for advanced fibrosis detection utilized in the development of algorithms could influence results and should be taken into account when comparing different clinical practice guidelines. The spectrum of blood-based tests utilized to detect at-risk MASH fibrosis is very wide and includes very easy, available, and cheap tests to more sophisticated and expensive ones. However, no big differences have been reported yet. Nevertheless, just one test fixing all is not plausible, and algorithm combining methods is mandatory. Despite more than a hundred noninvasive test (NITs) have been developed in the last years a few reached our clinical practice; some reason include (1) cost, (2) availability, (3) biological plausibility, (4) technology requirements; (5) acceptability, and (6) reliability. It is not easy to explain why the most utilized methods, like FIB4 or ELF were developed to detect fibrosis in hepatitis C or any etiology. Methods based on lipidomics like OWLLiver or proteomics like SomaScan must break many barriers beyond methodological concerns. Hepamet fibrosis score, despite designed in a large cohort of patients with biopsy-proven MASLD and including parameters with a high biological plausibility did not reach wide use.11 Noninvasive tests could be used to screen, stage, refer, confirm diagnosis, and monitor therapy and progression, and in each scenario must be carefully validated. No cost-effective analysis was included in the current study, and it could be a very important point to take into account in the decision-making. Moreover, it could impact the final design and building of the algorithm. Lastly, taking data from the current paper and drawing a Sankey diagram, we can detect that using FIB4 as a first-line test is accurate, and patients with FIB4 under 1.3 could avoid more studies. Transient elastography significantly reduced the burden of patients to be addressed by MRE in comparison with ELF (Figure 1).FIGURE 1: Sankey diagram showing the flux of patients according to scores in FIB4, VCTE, and MRE in AGA CPG (A) and FIB4, ELF, and MRE in AASLD CPG (B). Abbreviations: CPG, clinical practice guidelines; ELF, enhanced liver fibrosis; FIB4, Fibrosis-4; MRE, magnetic resonance elastography; VCTE, vibration-controlled transient elastography.In summary, around 1 out of 7–8 patients living with diabetes suffered from advanced fibrosis, and they could be accurately detected using these 2 algorithms. We do not reach the best algorithm, but we have 2 good pathways to detect advanced fibrosis, a key outcome in clinical practice.